The field of graph neural networks (GNNs) is witnessing significant advancements in handling uncertainty and missing data, particularly in federated learning settings. Recent studies are focusing on enhancing the reliability and interpretability of GNNs by integrating uncertainty quantification techniques such as Conformal Prediction and tensor-based topological learning. These methods aim to provide more robust predictions in the presence of missing neighbor information and non-exchangeable graph data. Additionally, there is a growing emphasis on developing models that can effectively manage and impute missing data within graph structures, ensuring consistency and explainability. The incorporation of orientation equivariance and invariance in GNN architectures is also advancing the field, enabling more precise modeling of both directed and undirected edge signals. Furthermore, the approximation of equivariance through multitask learning is being explored to reduce computational complexity without compromising performance. Lastly, advancements in differentiable structure learning are addressing inconsistencies and non-convexity issues, paving the way for more reliable methods in identifying underlying graph structures.

Noteworthy papers include one that extends Conformal Prediction to federated graph learning, effectively mitigating the impact of missing data, and another that introduces a novel tensor-based topological neural network for rigorous uncertainty quantification in graph classification tasks.